word embedding Italian Wikipedia Embedding [1]} trained using GloVe https://github.com/MartinoMensio/it_vectors_wiki_spacy, Fasttext with [2]} https://fasttext.cc/docs/en/pretrained-vectors.html trained using skip-gram model and with 300 dimensions, and TWITA embedding [3]}. For TWITA, two versions of the same tweets were generated. One preprocessing words where each vector has 100 dimensions, provided by [4]}https://github.com/mirkolai/evalita-sardistance/ and referred to as TWITA100. The other one trained by us without any preprocessing and each vector has 300 dimensions, referred to as TWITA300. We also experimented with multilingual BERT in Task A https://tfhub.dev/tensorflow/bert_multi_cased_L-12_H-768_A-12/2 [5]}.
